Drug repositioning for cancer in the era of AI, big omics, and real-world data
Copyright © 2022 Elsevier B.V. All rights reserved..
Drug repositioning in cancer has been pursued for years because of slowing drug development, increasing costs, and the availability of drugs licensed for other indications with anticancer effects in the laboratory. Repositioning has encountered obstacles due to generally insufficient single-agent clinical anticancer effects of licensed drugs and a subsequent reluctance by pharmaceutical companies to invest in phase III combination studies with them. Here we review potential machine learning/artificial intelligence (ML/AI) approaches for using real-world data (RWD) that could overcome the limitations of clinical trials and retrospective analyses. We outline a two-tiered filtering approach of identifying top-ranked drugs based on their drug-target binding affinity scores while considering their challenges and matching the top-ranked drugs with their top-ranked specific scenarios from among the multitude of real-world scenarios for efficacy and safety. This approach will generate RWD scenario-specific hypotheses that can be tested in randomized clinical trials with high probabilities of success.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:175 |
|---|---|
| Enthalten in: |
Critical reviews in oncology/hematology - 175(2022) vom: 15. Juli, Seite 103730 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wieder, Robert [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 20.06.2022 Date Revised 20.06.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.critrevonc.2022.103730 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM341762083 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM341762083 | ||
| 003 | DE-627 | ||
| 005 | 20231226012524.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.critrevonc.2022.103730 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1139.xml |
| 035 | |a (DE-627)NLM341762083 | ||
| 035 | |a (NLM)35654244 | ||
| 035 | |a (PII)S1040-8428(22)00154-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wieder, Robert |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Drug repositioning for cancer in the era of AI, big omics, and real-world data |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 20.06.2022 | ||
| 500 | |a Date Revised 20.06.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Elsevier B.V. All rights reserved. | ||
| 520 | |a Drug repositioning in cancer has been pursued for years because of slowing drug development, increasing costs, and the availability of drugs licensed for other indications with anticancer effects in the laboratory. Repositioning has encountered obstacles due to generally insufficient single-agent clinical anticancer effects of licensed drugs and a subsequent reluctance by pharmaceutical companies to invest in phase III combination studies with them. Here we review potential machine learning/artificial intelligence (ML/AI) approaches for using real-world data (RWD) that could overcome the limitations of clinical trials and retrospective analyses. We outline a two-tiered filtering approach of identifying top-ranked drugs based on their drug-target binding affinity scores while considering their challenges and matching the top-ranked drugs with their top-ranked specific scenarios from among the multitude of real-world scenarios for efficacy and safety. This approach will generate RWD scenario-specific hypotheses that can be tested in randomized clinical trials with high probabilities of success | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Drug Repositioning | |
| 650 | 4 | |a Electronic health records | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Omics | |
| 650 | 4 | |a Real world data | |
| 700 | 1 | |a Adam, Nabil |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Critical reviews in oncology/hematology |d 1996 |g 175(2022) vom: 15. Juli, Seite 103730 |w (DE-627)NLM012606006 |x 1879-0461 |7 nnns |
| 773 | 1 | 8 | |g volume:175 |g year:2022 |g day:15 |g month:07 |g pages:103730 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.critrevonc.2022.103730 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 175 |j 2022 |b 15 |c 07 |h 103730 | ||